A TinyMLaaS Ecosystem for Machine Learning in IoT: Overview and Research Challenges
Hiroshi Doyu, Roberto Morabito, Martina Brachmann
Abstract
Tiny Machine Learning (TinyML) is an emerging concept that concerns the execution of ML tasks on very constrained IoT devices. Although TinyML has generated a strong R&D interest around it, various challenges limit its effective execution in the constrained devices world, with the result of slowing down the development of a complete ecosystem around it. TinyML as-a-Service (TinyMLaaS) aims to fill the gap in this respect, with the definition of a set of guidelines that can enable an easier democratization of TinyML. In this paper, we describe how the "as-a-Service" model is bound to TinyML, by providing an overview of our concept and introducing the design requirements and building blocks that can make TinyMLaaS reality.